Datasets. We evaluate our recall loss on two popular large-scale outdoor semantic segmentation datasets, Synthia [1]} and Cityscapes [2]}. Synthia is a photorealistic synthetic dataset with different seasons, weather, and lighting conditions. Specifically, we use the Synthia-sequence Summer split for training (4400), validation (624), and testing (1272). Cityscapes consists of real photos of urban street scenes in several cities in Europe. It has 5000 annotated images for training and another 5000 for evaluation. We further show that recall loss is beneficial for feature learning in image classification on two large-scale long-tailed datasets including Place-LT [3]} and iNaturvspacealist2018 [4]}. Place-LT has 365 classes and long-tailed class distribution. It is created by sampling from the original balanced dataset [5]} following a Pareto distribution. iNaturalist2018 is a long-tailed image collection of natural species of 8142 classes.
